A content classification system (hereinafter may be referred to as ‘classification system’), in general, might be used to filter content from a large corpus to identify content in context of some information. The classification could also be used to flag the content in terms of belonging to a distinct class. For example, a content classification system could be used to capture sentiments from posts relating to one or more products or services, from various forums, including social platforms. The term, ‘post’, as used herein, means an amalgamation of sentences. The term, social platform, as used herein, means web-based services that allow members to construct a public or a semi-public profile within the boundary of that particular community. Classification of these posts could be used to evaluate a marketing strategy for a product or service. While a post is used as an example of content, it should be understood that any artifact with an amalgamation of sentences might be equally applicable.
A sentiment, in general, refers to a view or an opinion towards a situation or an event. Various methods have been proposed for evaluating sentiment from text. Conventionally, evaluation of sentiment from text within the posts has been performed using a rule based approach. Ruled-based approach applies a set of rules to pre-process text in order to aid classification by techniques such as, part-of-speech (hereinafter may be referred to as ‘POS’) tagging and keyword phrases to tag sentiment into distinct classes. Rule-based approach needs to be exhaustive and deriving sentiment from free form text using incomplete rules can result in erroneous evaluation. Evolving rule-base over time can be difficult as changing POS tagging rules may not be feasible for a corpus containing large number of posts. Further, predefined static rules might be infeasible to cover all classes of possible text examples while conceiving the system.
Another approach has been to use supervised learning methods where several features from a corpus of posts are derived to train supervised classifiers. Training might involve providing the system with a corpus and classifications for each post in the training corpus. The system then operates a training process wherein patterns are discovered in the training corpus between the classifications of various posts. Supervised approach can also be limiting for system evolution as it requires re-training the entire system when new evidence is provided.
While these systems are useful, it is difficult to use either of the two approaches when the number of posts is not sufficient to build an exhaustive set of rules or appropriately train a classifier. Further, sentiment evaluation might be subjective to a user and therefore the same post can convey different information to another user. Hence, there is a considerable need in the art for a more sophisticated system capable of managing sentiments based on multiple inputs into multidimensional categories.
Disclosed herein are improved systems and methods for sentiment management which is adaptive to a user.
The present invention is directed to overcoming these and other deficiencies in the art.